baseline_generation
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| baseline_generation [2020/04/26 05:18] – matsz | baseline_generation [2022/11/07 10:23] (current) – external edit 127.0.0.1 | ||
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| * The second example is that of FAPRI model, where a so-called melting down meeting is organised where the modellers responsible for specific parts of the system come together with market experts. Results are discussed, parameters and assumptions changed until there is consensus. Little is known about how the process works exactly, but both examples underline the interaction between model mechanisms and ex-ante expectations of market experts. | * The second example is that of FAPRI model, where a so-called melting down meeting is organised where the modellers responsible for specific parts of the system come together with market experts. Results are discussed, parameters and assumptions changed until there is consensus. Little is known about how the process works exactly, but both examples underline the interaction between model mechanisms and ex-ante expectations of market experts. | ||
| - | As is the case in other agencies, the CAPRI baseline is also fed by external (“expert”) forecasts, as well as by trend forecasts using data from the national ‘COCO’ and regionalized CAPREG databases (Chapters | + | As is the case in other agencies, the CAPRI baseline is also fed by external (“expert”) forecasts, as well as by trend forecasts using data from the national ‘COCO’ and regionalized CAPREG databases (sections |
| =====Overview of CAPRI baseline processes===== | =====Overview of CAPRI baseline processes===== | ||
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| The forecast tool CAPTRD uses the consolidated national and regional time series from COCO and CAPREG together with external projections from the AgLink model. The result is a projection for the key variables in the agricultural sector (activity levels and market balances) of all regions in the supply models (EU+) that is consistent with the supply model equations. | The forecast tool CAPTRD uses the consolidated national and regional time series from COCO and CAPREG together with external projections from the AgLink model. The result is a projection for the key variables in the agricultural sector (activity levels and market balances) of all regions in the supply models (EU+) that is consistent with the supply model equations. | ||
| - | - Next task is the market model calibration. That task uses the same AgLink projections, | + | - Next task is the market model calibration. That task uses the same AgLink projections, |
| - The third task is the calibration of the supply models. This step also uses the regional data base, regional trends, and policy files, and calibrates various technical and behavioural economic parameters of the supply models so that the projected regional production is the optimal production at the producer prices coming from the market model calibration. | - The third task is the calibration of the supply models. This step also uses the regional data base, regional trends, and policy files, and calibrates various technical and behavioural economic parameters of the supply models so that the projected regional production is the optimal production at the producer prices coming from the market model calibration. | ||
| - Finally, the modeller typically wants to perform a simulation using all the calibrated parameters and projected data. The purpose is twofold: to verify that the calibration of the baseline worked as intended and to generate all reports for inspection in the GUI. | - Finally, the modeller typically wants to perform a simulation using all the calibrated parameters and projected data. The purpose is twofold: to verify that the calibration of the baseline worked as intended and to generate all reports for inspection in the GUI. | ||
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| ===Constraints relating to market balances and yields=== | ===Constraints relating to market balances and yields=== | ||
| - | Closed market balances (CAPTRD eq. MBAL_) define the first set of constraints and state that the sum of imports (IMPT) and production (GROF) must be equal to the sum of feed (FEDM) and seed (SEDM) use, human consumption (HCOM), processing (INDM, | + | Closed market balances (CAPTRD eq. MBAL_ ) define the first set of constraints and state that the sum of imports (IMPT) and production (GROF) must be equal to the sum of feed (FEDM) and seed (SEDM) use, human consumption (HCOM), processing (INDM, |
| \begin{align} | \begin{align} | ||
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| **Figure 11: Pork production in Hungary as an example for merging medium run and long run a priori information in the CAPRI baseline approach** | **Figure 11: Pork production in Hungary as an example for merging medium run and long run a priori information in the CAPRI baseline approach** | ||
| - | {{::figure11.png? | + | {{::figure_11.png? |
| The example has been chosen because historical trends (and Aglink-COSIMO projections) on the one hand and long run expectations differ markedly. This is not unusual because medium run forecasts often give a stronger weight to recent production trends, often indicating a stagnating or declining production in the EU, whereas the long run studies tend to focus on the global growth of food demand in the coming decades. The simple trends (filled triangles) would evidently give unreasonable, | The example has been chosen because historical trends (and Aglink-COSIMO projections) on the one hand and long run expectations differ markedly. This is not unusual because medium run forecasts often give a stronger weight to recent production trends, often indicating a stagnating or declining production in the EU, whereas the long run studies tend to focus on the global growth of food demand in the coming decades. The simple trends (filled triangles) would evidently give unreasonable, | ||
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| Constraints, | Constraints, | ||
| - | Next, FAO data on the non-European countries as well as the trade flows among all of the countries (country trade blocks) accounted for in CAPRI are loaded. These FAO data together with the European data, which has already been subjected to certain adjustments as described in the previous paragraph, undergo the, so-called, data preparation step. This process is controlled by C:/ | + | Next, FAO data on the non-European countries as well as the trade flows among all of the countries (country trade blocks) accounted for in CAPRI are loaded. These FAO data together with the European data, which has already been subjected to certain adjustments as described in the previous paragraph, undergo the, so-called, data preparation step. This process is controlled by C:/ |
| - | Together with the data, equations of the CAPRI market module are loaded. They are described in detail in section [[Market module for agricultural outputs]]. These equations include behavioural functions for market demand including expenditure function, feed demand, blocks for dairy products, oilseeds processing and biofuels, netput functions, trade equations and balances, equations for prices and price transmission, | + | Together with the data, equations of the CAPRI market module are loaded. They are described in detail in section [[scenario simulation#Market module for agricultural outputs]]. These equations include behavioural functions for market demand including expenditure function, feed demand, blocks for dairy products, oilseeds processing and biofuels, netput functions, trade equations and balances, equations for prices and price transmission, |
| ===Data balancing=== | ===Data balancing=== | ||
| - | After data preparation, | + | After data preparation, |
| //Data balancing for the base year// \\ | //Data balancing for the base year// \\ | ||
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| Data calibration for the base year aims at modifying the base year data to fit the system of equations of the market module. Some of the parameters defined in Stage I (e.g., p_rhoX) as well as parameter values and bounds defined at this stage are used. For example, starting points and corridors for quantity variables are set (e.g., calculating of world production to define correction corridor for calibration of production/ | Data calibration for the base year aims at modifying the base year data to fit the system of equations of the market module. Some of the parameters defined in Stage I (e.g., p_rhoX) as well as parameter values and bounds defined at this stage are used. For example, starting points and corridors for quantity variables are set (e.g., calculating of world production to define correction corridor for calibration of production/ | ||
| - | With the file C:/ | + | With the file C:/ |
| The model that calibrates base year data (MODEL m_calMarketBas) is defined in cal_models.gms file as well and includes almost all equations of the market model. In particular: equations for processing margin for dairy products (ProcMargM_), | The model that calibrates base year data (MODEL m_calMarketBas) is defined in cal_models.gms file as well and includes almost all equations of the market model. In particular: equations for processing margin for dairy products (ProcMargM_), | ||
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| \end{equation} | \end{equation} | ||
| - | where SSQ is an artificial variable to be minimized, indices RMS, XXX, BAS and i indicate, respectively, | + | where SSQ is an artificial variable to be minimized, indices RMS, XXX, BAS and i indicate, respectively, |
| The process of model solving is navigated with C:/ | The process of model solving is navigated with C:/ | ||
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| After solving the MODEL m_calMarketBas, | After solving the MODEL m_calMarketBas, | ||
| - | Data balancing for the simulation year \\ | + | //Data balancing for the simulation year// \\ |
| Aim of data calibration for the simulation year aims at generating such quantity, price and other market values (see list below) for the simulation year that they fit the system of equations of the market module and variable and parameter lower and upper bounds, as well as remain as close as possible to the values to which they are calibrated (e.g., trends, estimated with growth rates from the base year, Aglink-COSIMO values, GLOBIOM values etc.). Thus process, basically, follows similar approach as for the base year. There are, however, a few differences. The main is that the model used for calibration is MODEL m_calMarketFin. As the model for base year calibration (MODEL m_calMarketBas), | Aim of data calibration for the simulation year aims at generating such quantity, price and other market values (see list below) for the simulation year that they fit the system of equations of the market module and variable and parameter lower and upper bounds, as well as remain as close as possible to the values to which they are calibrated (e.g., trends, estimated with growth rates from the base year, Aglink-COSIMO values, GLOBIOM values etc.). Thus process, basically, follows similar approach as for the base year. There are, however, a few differences. The main is that the model used for calibration is MODEL m_calMarketFin. As the model for base year calibration (MODEL m_calMarketBas), | ||
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| At first, parameters for land use market are calculated based on data from FAO world food market model. Among them are land use classes, crop yields, land demand of non-crop activities, areas used for fodder and average land price, total energy use for feeding and producer price of feed. Next, starting elasticity values, as well as their lower and upper bounds are loaded (e.g., demand elasticities used in SPEL/MFSS). Finally, elasticities are trimmed. | At first, parameters for land use market are calculated based on data from FAO world food market model. Among them are land use classes, crop yields, land demand of non-crop activities, areas used for fodder and average land price, total energy use for feeding and producer price of feed. Next, starting elasticity values, as well as their lower and upper bounds are loaded (e.g., demand elasticities used in SPEL/MFSS). Finally, elasticities are trimmed. | ||
| - | Elasticities trimming is controlled by C:/ | + | Elasticities trimming is controlled by C:/ |
| Human consumption elasticities are estimated with MODEL m_trimDem by minimizing absolute squares between given and calibrated elasticities (FitElas_). Apart from the objective function the model includes several equations related to the definition of the demand system as Generalized Leontief, homogeniety of degree zero for elasticities in prices, additivity of income elasticities weighted with budget shares and elasticities for total calorie intake. | Human consumption elasticities are estimated with MODEL m_trimDem by minimizing absolute squares between given and calibrated elasticities (FitElas_). Apart from the objective function the model includes several equations related to the definition of the demand system as Generalized Leontief, homogeniety of degree zero for elasticities in prices, additivity of income elasticities weighted with budget shares and elasticities for total calorie intake. | ||
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| The file C:/ | The file C:/ | ||
| - | ====tage IV: Initialization and test run==== | + | ====Stage IV: Initialization and test run==== |
| After the behavioural blocks of the market model are calibrated (one-by-one), | After the behavioural blocks of the market model are calibrated (one-by-one), | ||
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| ====Introduction==== | ====Introduction==== | ||
| - | The supply side models of the CAPRI simulation tool are programming models with an objective function. If we want the optimal solution to coincide with the forecast | + | The supply side models of the CAPRI simulation tool are programming models with an objective function. If we want the optimal solution to coincide with the forecast |
| - Elements not projected so far but entering the constraints of the supply models (e.g. feed, fertilization) must be defined in such way that constraints are feasible, | - Elements not projected so far but entering the constraints of the supply models (e.g. feed, fertilization) must be defined in such way that constraints are feasible, | ||
baseline_generation.1587878319.txt.gz · Last modified: 2022/11/07 10:23 (external edit)